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Abstract

The work presented in this paper provides an assessment of how deep the content-based recommendation algorithm described domain movies and TV shows metadata information can influence. The method used in order to verify the results, and to ensure that they are not affected, used data set metadata itself, rather than by the different data sets and indicators. The combination of different metadata elements provides usually better results when compared with metadata used separately.

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2. Related Work

Recommendation system system's ability to provide advice or services, products or content, there are several different programs (Burke, 2007; Resnick & Varian, 1997) also indicates the potential between someone. One example is a book recommendation system to help users select a book to read (Cremonesi, Koren, & Turrin, 2010; Fouss & Serens, 2008; Sarwar, Karypis, Konstan, & Riedl, 2000).

A standard approach is to provide a project ranked list, trying to predict what the most appropriate product or service, according to the user's preferences. To create user profiles, user preferences may be expressly (such as product, genre preferences rating) or by the system by interpreting user (Ricci, Rokach, Shapira, Kantor, & Paul, 2010), inferred.

Although the first recommendation system since the late 1970s, only in the early 1990s, the first commercial application to deploy these types of systems (Adomavicius & Tuzhilin, 2005). Currently, Internet sites such as Amazon, eBay or YouTube using the system highly recommend them for their part of the service users. For example, Amazon uses a recommendation system for each customer personalized online store.

In addition to these examples, recommendation system also applies to many other areas, including books, movies, TV Guide, music, news articles. In the field of music, Pandora and Last.fm are known to the system, they provide users with a new proposal on the basis that they had heard and your favorite songs. Netflix, business services, offers movies and TV shows, gifts predictive rating for each display video clips to help users decide hire. Movilens (Miller, Albert, Lam, Konstan & Riedl, 2003), a free, non-commercial, the tool also provides the area's movie recommendation service.